arxiv arXiv cs.LG · 53d ago · research

NoiseTilt: Noise-Tilted Reverse Kernels for Diffusion Reward Alignment

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NoiseTilt introduces NTRK, a reward-guided diffusion sampler that injects reward gradients via the noise term without altering the reverse kernel. By using a whitening operator, NTRK safely biases noise toward high reward, preserving sample quality while maintaining strong guidance. On aesthetic generation, NTRK achieves superior reward performance with 25 NFEs, reducing compute by 20× compared to state-of-the-art baselines.

Importance 3/3 Beats a top-lab benchmark New feature vs. leaders New harness with differentiators arXiv cs.LG OpenAI Google DeepMind Hugging Face Evaluation & benchmarks Image generation Reasoning models

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arxiv arXiv cs.AI · 51d ago

Lean as Process-Verified Reward Oracle in RL for Theorem Proving

This work shows that Lean can serve as a symbolic process oracle, providing fine-grained, verified feedback during reinforcement learning. By parsing proof attempts into tactic sequences and using Lean's elaboration to mark sound steps and first failures, the system generates dense, type-theoretic reward signals. Experiments demonstrate tactic-level supervision outperforms outcome-only methods on benchmarks like MiniF2F and ProofNet, highlighting Lean's role as both evaluator and training reward source.

arxiv arXiv cs.AI · 51d ago · 1 view

Learnable Global Merging for Variable-Length Tokenization in Diffusion Transformers

A novel variable-length tokenizer uses learnable global merging to enable cross-length representation alignment in diffusion models. This data-independent approach overcomes position-dependent semantics and improves the quality-compute trade-off on ImageNet 256×25-6 generation compared to prior methods.

arxiv arXiv cs.AI · 51d ago

MACR: Explicit Conflict Resolution for LLM Inference

MACR introduces a multi-agent reasoning framework to resolve knowledge conflicts in LLM inference by jointly assessing internal and external knowledge. It uses semantic entropy to measure confidence and employs three specialized agents to induce rules, detect conflicts, and resolve inconsistencies across contexts. Empirical results show MACR outperforms state-of-the-art methods and provides interpretable conflict resolutions.

arxiv arXiv cs.LG · 51d ago · 1 view

VIMPO: Critic-Free Policy Optimization for LLMs

VIMPO introduces a critic-free policy optimization method that derives a policy-implied value function from KL-regularized reinforcement learning. It enables verifiable reward incorporation without training a critic and outperforms GRPO on mathematical benchmarks, especially under noisy rewards.

arxiv arXiv cs.CL · 51d ago · 4 views

Bayesian Curriculum Learning on LLM Latent Manifolds

Manifold Bandits introduces Bayesian Manifold Curriculum (BMC), a framework that models problem sampling as a structured bandit problem in LLMs' latent space. BMC organizes tasks into a hierarchical tree and uses Bayesian learning to guide sampling, revealing tradeoffs between learning signal, task diversity, and evaluation relevance. Prioritizing difficulty alone fails to achieve strong downstream performance, underscoring the need for structure and type-aware sampling.